Supplemental practical concepts · MLD-1284
Uncertainty sampling
An active-learning strategy that requests labels for examples the current model finds uncertain.
Original reference page · detailed expansion pending. Original definitions, examples, notes and citations are preserved below. New examples are illustrative, not benchmark results.
Original example
Prioritize unlabeled examples near a decision boundary.
Original caveat
Uncertainty can reflect noise rather than useful learning opportunities.
Context and aliases
Level: Intermediate. Stages: Training · Inference · Evaluation.
Tags: ML · DL · LLM · SLM.
Aliases: None recorded.
Related concepts
Concepts linking here
Original references
- Deep Learning: online textbook ↗ · Consulted
- Transformers glossary ↗ · Consulted
- OpenAI Spinning Up: reinforcement learning ↗ · Consulted
- PyTorch documentation ↗ · Reference link
- Original reference URL ↗